• Korean Journal of Radiology
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• v.22 no.6
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• pp.994-1004
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• 2021

Objective: To extract pulmonary and cardiovascular metrics from chest CTs of patients with coronavirus disease 2019 (COVID-19) using a fully automated deep learning-based approach and assess their potential to predict patient management. Materials and Methods: All initial chest CTs of patients who tested positive for severe acute respiratory syndrome coronavirus 2 at our emergency department between March 25 and April 25, 2020, were identified (n = 120). Three patient management groups were defined: group 1 (outpatient), group 2 (general ward), and group 3 (intensive care unit [ICU]). Multiple pulmonary and cardiovascular metrics were extracted from the chest CT images using deep learning. Additionally, six laboratory findings indicating inflammation and cellular damage were considered. Differences in CT metrics, laboratory findings, and demographics between the patient management groups were assessed. The potential of these parameters to predict patients' needs for intensive care (yes/no) was analyzed using logistic regression and receiver operating characteristic curves. Internal and external validity were assessed using 109 independent chest CT scans. Results: While demographic parameters alone (sex and age) were not sufficient to predict ICU management status, both CT metrics alone (including both pulmonary and cardiovascular metrics; area under the curve [AUC] = 0.88; 95% confidence interval [CI] = 0.79-0.97) and laboratory findings alone (C-reactive protein, lactate dehydrogenase, white blood cell count, and albumin; AUC = 0.86; 95% CI = 0.77-0.94) were good classifiers. Excellent performance was achieved by a combination of demographic parameters, CT metrics, and laboratory findings (AUC = 0.91; 95% CI = 0.85-0.98). Application of a model that combined both pulmonary CT metrics and demographic parameters on a dataset from another hospital indicated its external validity (AUC = 0.77; 95% CI = 0.66-0.88). Conclusion: Chest CT of patients with COVID-19 contains valuable information that can be accessed using automated image analysis. These metrics are useful for the prediction of patient management.

• Korean Journal of Radiology
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• v.22 no.4
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• pp.652-662
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• 2021

Objective: To investigate the association between CT imaging features and survival outcomes in patients with primary invasive mucinous adenocarcinoma (IMA). Materials and Methods: Preoperative CT image findings were consecutively evaluated in 317 patients with resected IMA from January 2011 to December 2015. The association between CT features and long-term survival were assessed by univariate analysis. The independent prognostic factors were identified by the multivariate Cox regression analyses. The survival comparison of IMA patients was investigated using the Kaplan-Meier method and propensity scores. Furthermore, the prognostic impact of CT features was assessed based on different imaging subtypes, and the results were adjusted using the Bonferroni method. Results: The median follow-up time was 52.8 months; the 5-year disease-free survival (DFS) and overall survival rates of resected IMAs were 68.5% and 77.6%, respectively. The univariate analyses of all IMA patients demonstrated that 15 CT imaging features, in addition to the clinicopathologic characteristics, significantly correlated with the recurrence or death of IMA patients. The multivariable analysis revealed that five of them, including imaging subtype (p = 0.002), spiculation (p < 0.001), tumor density (p = 0.008), air bronchogram (p < 0.001), emphysema (p < 0.001), and location (p = 0.029) were independent prognostic factors. The subgroup analysis demonstrated that pneumonic-type IMA had a significantly worse prognosis than solitary-type IMA. Moreover, for solitary-type IMAs, the most independent CT imaging biomarkers were air bronchogram and emphysema with an adjusted p value less than 0.05; for pneumonic-type IMA, the tumors with mixed consolidation and ground-glass opacity were associated with a longer DFS (adjusted p = 0.012). Conclusion: CT imaging features characteristic of IMA may provide prognostic information and individual risk assessment in addition to the recognized clinical predictors.

• Progress in Medical Physics
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• v.24 no.2
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• pp.85-91
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• 2013

At present, megavoltage computed tomography (MVCT) is the only method used to correct the position of tomotherapy patients. MVCT produces extra radiation, in addition to the radiation used for treatment, and repositioning also takes up much of the total treatment time. To address these issues, we suggest the use of a video image-guided setup (VIGS) system for correcting the position of tomotherapy patients. We developed an in-house program to correct the exact position of patients using two orthogonal images obtained from two video cameras installed at $90^{\circ}$ and fastened inside the tomotherapy gantry. The system is programmed to make automatic registration possible with the use of edge detection of the user-defined region of interest (ROI). A head-and-neck patient is then simulated using a humanoid phantom. After taking the computed tomography (CT) image, tomotherapy planning is performed. To mimic a clinical treatment course, we used an immobilization device to position the phantom on the tomotherapy couch and, using MVCT, corrected its position to match the one captured when the treatment was planned. Video images of the corrected position were used as reference images for the VIGS system. First, the position was repeatedly corrected 10 times using MVCT, and based on the saved reference video image, the patient position was then corrected 10 times using the VIGS method. Thereafter, the results of the two correction methods were compared. The results demonstrated that patient positioning using a video-imaging method ($41.7{\pm}11.2$ seconds) significantly reduces the overall time of the MVCT method ($420{\pm}6$ seconds) (p<0.05). However, there was no meaningful difference in accuracy between the two methods (x=0.11 mm, y=0.27 mm, z=0.58 mm, p>0.05). Because VIGS provides a more accurate result and reduces the required time, compared with the MVCT method, it is expected to manage the overall tomotherapy treatment process more efficiently.

• The Journal of Korean Society for Radiation Therapy
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• v.22 no.1
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• pp.11-18
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• 2010

Purpose: In every time radiation therapy set up errors occur because internal anatomical organs move due to breathing and change of patient's position. These errors may affect the change of dose distribution between target area and normal structure. This study investigates the usefulness of body-fix in clinical treatment. Materials and Methods: Among 55~60 aged male patients who has hepatocellular carcinoma in area of liver's couinaud classification, we chose 10 patients and divided two groups by using body-fix or not. When applying body-fix, we maintained a vacuum of 80 mbar pressure by using vacuum pump (Medical intelligence, Germany). Patients had free breathing with supine position. After working to fuse and consist MV-CT (megavoltage computed tomography) with KV-CT (kilovoltage computed tomography) obtained by 5 times treatments, we compared and analyzed set up errors occurred to (Right to Left, RL) of X axis, (Anterioposterio, AP) of Z axis, (Cranicoudal, CC) of Y axis. Results: Average Set up errors through image fusion showed that group A moved $0.3{\pm}1.1\;mm$ (Cranicoudal, CC), $-1.1{\pm}0.7\;mm$ (Right to Left, RL), $-0.2{\pm}0.7\;mm$ (Anterioposterio, AP) and group B moved $0.62{\pm}1.94\;mm$ (Cranicoudal, CC), $-3.62{\pm}1.5\;mm$ (Right to Left, RL), $-0.22{\pm}1.2\;mm$ (Anterioposterio, AP). Deviations of X, Y and Z axis directions by applying body-fix indicated that maximum X axis was 5.5 mm, Y axis was 19.8 mm and Z axis was 3.2 mm. In relation to analysis of error directions, consistency doesn't exist for every patient but by using body-fix showed that the result of stable aspect in spite of changes of everyday's patient position and breathing. Conclusion: Using body-fix for liver cancer patient is considered effectively for tomotherapy. Because deviations between group A and B exist but they were stable and regular.

• Journal of Korea Multimedia Society
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• v.23 no.6
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• pp.729-737
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• 2020

Screening with low-dose spiral computed tomography (LDCT) has been shown to reduce lung cancer mortality by about 20% when compared to standard chest radiography. One of the problems arising from screening programs is that large amounts of CT image data must be interpreted by radiologists. To solve this problem, automated detection of pulmonary nodules is necessary; however, this is a challenging task because of the high number of false positive results. Here we demonstrate detection of pulmonary nodules using six off-the-shelf convolutional neural network (CNN) models after modification of the input/output layers and end-to-end training based on publicly databases for comparative evaluation. We used the well-known CNN models, LeNet-5, VGG-16, GoogLeNet Inception V3, ResNet-152, DensNet-201, and NASNet. Most of the CNN models provided superior results to those of obtained using customized CNN models. It is more desirable to modify the proven off-the-shelf network model than to customize the network model to detect the pulmonary nodules.

• The Journal of the Korea Contents Association
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• v.7 no.4
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• pp.206-212
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• 2007

To evaluate the HU value of the IV catheter fragment of CT on the accuracy and size in the peripheral vein. Pilot study of profile and table functions on PC by software was calculated of HU value of IV catheter fragment. This study demonstrates the utility of volume rendering technique to localize a small, subtle IV catheter, which can easily be reformatted of MDCT reformations. IV catheter fragment optimal image described as threshold range. Volume rendering of HU using a MDCT is an excellent method for evaluation the IV catheter fragment in three dimension.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.8
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• pp.913-920
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• 2012

An efficient 3D finite element walking model that considers the detailed shapes of muscles, ligaments, bones, skin, and soles was developed based on a real computed tomography (CT) scan image of a foot, and nonlinear contact analyses were performed to investigate pressure changes. The highest pressure occurs at the rear bottom of the foot when standing and walking. The pressure on the outsole with a curved foot bottom surface is lessened and distributed over a wider area than in the case of a flat outsole. The result shows that a shoe sole shape optimized for diabetes patients can relieve the foot pressure concentration and prevent further worsening of symptoms.

• Journal of Korean Neurosurgical Society
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• v.30 no.9
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• pp.1144-1149
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• 2001

Schwannomas of the jugular foramen, originating from the glossopharyngeal nerve, vagus and accessory nerve represent approximately 0.17-0.72% of all intracranial tumor, and consists of 1.4-2.9% of all intracranial schwannomas. The clinical presentation of these tumors varies significantly according to originated nerve and it's growth pattern. Magnetic resonance(MR) image and temporal bone computed tomography(CT) scan have a major role for diagnosis of such tumor. The treatment of choice is total resection whenever possible. Generally, suboccipital approach is sufficient for the removal of the tumor, but in case with large size, combination of resection of petrous part of temporal bone with or without transection of sigmoid sinus is may be necessory. We have recently experienced one case of giant jugular foramen schwannoma and postoperative fatal complication in a 34-year-old male who was treated with combined posterior petrous and suboccipital approach with transection of sigmoid sinus

• Archives of Plastic Surgery
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• v.32 no.6
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• pp.796-797
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• 2005

In plastic and reconstructive craniomaxillofacial surgery, careful preoperative planning is essential to get a successful outcome. Many craniomaxillofacial surgeons have used imaging modalities like conventional radiographs, computed tomography(CT) and magnetic resonance imaging(MRI) for supporting the planning process. But, there are a lot of limitations in the comprehension of the surgical anatomy with these modalities. Medical models made with rapid prototyping (RP) technique represent a new approach for preoperative planning and simulation surgery. With rapid prototyping models, surgical procedures can be simulated and performed interactively so that surgeon can get a realistic impression of complex structures before surgical intervention. The great advantage of rapid prototyping technique is the precise reproduction of objects from a 3-dimensional reconstruction image as a physical model. Craniomaxillofacial surgeon can establish treatment strategy through preoperative simulation surgery and predict the postoperative result.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.183-189
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• 2023

A stroke is a medical disease where a blood vessel in the brain ruptures, causes damage to the brain. If the flow of blood and different nutrients to the brain is intermittent, symptoms may occur. Stroke is other reason for loss of life and widespread disorder. The prevalence of stroke is high in growing countries, with ischemic stroke being the high usual category. Many of the forewarning signs of stroke can be recognized the seriousness of a stroke can be reduced. Most of the earlier stroke detections and prediction models uses image examination tools like CT (Computed Tomography) scan or MRI (Magnetic Resonance Imaging) which are costly and difficult to use for actual-time recognition. Machine learning (ML) is a part of artificial intelligence (AI) that makes software applications to gain the exact accuracy to predict the end results not having to be directly involved to get the work done. In recent times ML algorithms have gained lot of attention due to their accurate results in medical fields. Hence in this work, Stroke disease identification system by using Machine Learning algorithm is presented. The ML algorithm used in this work is Artificial Neural Network (ANN). The result analysis of presented ML algorithm is compared with different ML algorithms. The performance of the presented approach is compared to find the better algorithm for stroke identification.